• Bulletin of the Korean Mathematical Society
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• v.54 no.3
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• pp.763-787
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• 2017

A graph is called 1-planar if it can be drawn in the Euclidean plane ${\mathbb{R}}^2$ such that each edge is crossed by at most one other edge. The weight of an edge is the sum of degrees of two ends. It is known that every planar graph of minimum degree ${\delta}{\geq}3$ has an edge with weight at most 13. In the present paper, we show the existence of edges with weight at most 25 in 3-connected 1-planar graphs.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2001.05a
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• pp.78-82
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• 2001

Impact behaviors of Aluminum Honeycombs Sandwich Panel(AHSP) by drop weight test were investigated. Two types of specimens with 1/2" and 1/4" cell size were tested by two impactors which are weight of $5.25\textrm{kg}_{\textrm{f}}$ and $11.9\textrm{kg}_{\textrm{f}}$. Parametric studies were achieved including the impactor weight and impact sites which consist face, long-edge, short-edge, and point of the specimen. Face one of impact sites was the strongest and short-edge one of impact sites was the weakest. The damaged area of AHSP was enlarged with the increase of impactor weight that is equal to impact energy. After 3 point bending test, fracture modes of AHSP were analyzed with AE counts. Lower facesheet was fractured in the long-edge direction and then separated between facesheet and core. In the short-edge direction after core wrinkled, lower facesheet tear occurred. Impact behavior by FE analysis were increased localized damage in fast velocity because the faster velocity of the impact was, the smaller the stress of core was. Consequently, impactor weight had an effect on widely damaged area, while the impact velocity was caused on the localized damaged area.aged area.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.35-42
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• 2014

This paper suggests a method that obtains the minimum spanning tree (MST) far more easily and rapidly than the present ones. The suggested algorithm, firstly, simplifies a graph by means of reducing the number of edges of the graph. To achieve this, it applies a method of eliminating the maximum weight edge if the valency of vertices of the graph is equal to or more than 3. As a result of this step, we can obtain the reduced edge population. Next, it applies a method in which the maximum weight edge is eliminated within the cycle. On applying the suggested population minimizing and maximum weight edge deletion algorithms to 9 various graphs, as many as the number of cycles of the graph is executed and MST is easily obtained. It turns out to lessen 66% of the number of cycles and obtain the MST in at least 2 and at most 8 cycles by only deleting the maximum weight edges.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.886-888
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• 2013

An edge includes information of objects such as magnitude, orientation, and location. Conventional edge detection methods to detect those edge are methods using Sobel, Prewitt, Roberts, Laplacian operator. Existing methods use fixed weighted mask to detect edges, and their edge detection characteristics are insufficient. Therefore, to remedy weakness of conventional methods, in this paper, an edge detection algorithm using weight with standard deviation and thresholds is proposed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.22-24
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• 2021

This paper is a proposal for edge weight prediction using neural networks to graph configurations of nodes and edges. Brand is one of the components of society. and one of the brand's most important strategies is geographical location strategy. This paper is focus on that strategy. In This paper propose two things: 1) Graph Configuration. node consists of brand store, edge consists of store-to-store relationships and edge weight consists of actual walk and drive distance values. 2) numbering edges and training neural networks to predict next store distance values. It is expected to be useful in analyzing successful brand geographical location strategies.

• Journal of the Korea Society of Computer and Information
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• v.20 no.3
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• pp.107-113
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• 2015

This paper presents a polynomial time algorithm to the minimum cardinality feedback edge set and minimum weight feedback edge set problems. The algorithm makes use of the property wherein the sum of the minimum spanning tree edge set and the minimum feedback edge set equals a given graph's edge set. In other words, the minimum feedback edge set is inherently a complementary set of the former. The proposed algorithm, in pursuit of the optimal solution, modifies the minimum spanning tree finding Kruskal's algorithm so as to arrange the weight of edges in a descending order and to assign cycle-deficient edges to the maximum spanning tree edge set MXST and cycle-containing edges to the feedback edge set FES. This algorithm runs with linear time complexity, whose execution time corresponds to the number of edges of the graph. When extensively tested on various undirected graphs both with and without the weighed edge, the proposed algorithm has obtained the optimal solutions with 100% success and accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.412-434
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• 2023

This study discusses the high-order diffusion method in the wavelet domain. It aims to improve the edge protection capability of the high-order diffusion method using wavelet coefficients that can reflect image information. During the first step of the proposed diffusion method, the wavelet packet decomposition is a more refined decomposition method that can extract the texture and structure information of the image at different resolution levels. The high-frequency wavelet coefficients are then used to construct the edge detection function. Subsequently, because accurate wavelet coefficients can more accurately reflect the edges and details of the image information, by introducing the idea of state weight, a scheme for recovering wavelet coefficients is proposed. Finally, the edge detection function is constructed by the module of the wavelet coefficients to guide high-order diffusion, the denoised image is obtained. The experimental results showed that the method presented in this study improves the denoising ability of the high-order diffusion model, and the edge protection index (SSIM) outperforms the main methods, including the block matching and 3D collaborative filtering (BM3D) and the deep learning-based image processing methods. For images with rich textural details, the present method improves the clarity of the obtained images and the completeness of the edges, demonstrating its advantages in denoising and edge protection.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.3
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• pp.20-28
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• 2000

Air wiping technique is widely used because of easy and efficient coating control in present CGL The coating weight is controlled by nozzle header pressure. strip line speed, and the distance between strip and nozzle. Coating defects are resulted from the unbalance of these control factors and the inaccuracy of coating equipments. We investigates the main cause of coating defects, such as edge overcoating and coating deviation through various experiments. It is found that the edge overcoating is mainly come from nozzle lip type, and the coating deviation is caused by the unbalance of dynamic pressure.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2441-2449
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• 2015

In this digital information era, utilization of images are essential for various media, and the edge is an important characteristical information of an object in images that includes the size, location, direction and etc. Many domestic and international studies are being conducted in order to detect these edge. Existing edge detection methods include Sobel, Prewitt, Roberts, Laplacian, LoG and etc. which apply fixed weight value. As these existing edge detection methods apply fixed weight mask to the image, edge detection characteristic appears slightly insufficient. Accordingly, in order to supplement these problems, this study used bottom-hat transformation from mathematical morphology and opening operation in improving the image and proposed an algorithm that detects for the edge after calculating mask-based gradient. And to evaluate the performance of the proposed algorithm, a comparison was made against the existing Sobel, Roberts, Prewitt, Laplacian, LoG edge detection methods, in illustrating visual images, and similarities were compared by calculating the MSE value based on the standard of each image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.136-138
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• 2022

Edge detection includes information such as the shape, position, size, and material of an object with respect to an image, and is a very important factor in analyzing the characteristics of the image. Existing edge detection methods include Sobel edge detection filter, Roberts edge detection filter, Prewitt edge detection filter, and LoG (Lapacian of Gaussian) using secondary differentials. However, these methods have a disadvantage in that the edge detection results are somewhat insufficient because a fixed weight mask is applied to the entire image area. Therefore, in this paper, we propose an edge detection algorithm that increases edge detection characteristics by considering the center pixel in the mask. In addition, in order to confirm the proposed edge detection performance, it was compared through simulation result images.